Cited by 321Liver Disease Diagnosis and TreatmentAdvanced MRI Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging

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Quantification of Liver Fat Content with CT and MRI: State of the Art

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Starekova, Jitka, Hernando, Diego, Pickhardt, Perry J., Reeder, Scott B.·2021·Radiology

DOI 10.1148/radiol.2021204288PMC PMC8574059

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Abstract

Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.

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“This definition is based on histologic studies on liver steatosis [12 Since a fa tt y kidney is not clearly de ﬁ ned in the literature and since there is no research on the correlation between MRI ﬁ ndings and histologic structure in this regard, we decided to set a threshold of a fat fraction of…”
BackgroundMethodInfluential
Association Between the Hepatic Steatosis Index and Risk of Incident Type 2 Diabetes Mellitus in the Normoglycemic Population:A Longitudinal Prospective Study in Japan
Si, R., Xiao, J., Zheng, K., Yin, Y., & Li, Y. (2024) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s462459 · Cited by 14
“30,31 In recent years, scholars have also discovered the value of HSI in other chronic diseases.”
Background
Photon-counting CT–derived Quantification of Hepatic Fat Fraction: A Clinical Validation Study
Dell, T., Mesropyan, N., Layer, Y. C., Tischler, V., Weinhold, L., Chang, J., Jansen, C., Schmidt, B. T., Jürgens, M., Isaak, A., et al. (2025) · Radiology · doi:10.1148/radiol.241677 · Cited by 13
Liver MRI–derived Proton Density Fat Fraction Predicts Diabetes Risk in Patients with Obesity: A Dose-Response Study
Deng, M., Li, Z., Sun, L., Wang, H., Tang, J., Liu, Y., Chen, X., Xu, H., & Dai, Z. (2025) · Radiology · doi:10.1148/radiol.250026 · Cited by 13
Current Update on Nomenclature, Diagnosis, and Management of Metabolic Dysfunction–associated Steatotic Liver Disease: Radiologists’ Perspective
Osta, E., Rios, F. S., Jaganathan, S., Thomas, C., Tsai, E., Surabhi, V. R., Prasad, S. R., & Katabathina, V. S. (2025) · Radiographics · doi:10.1148/rg.240221 · Cited by 13
Development and validation of multivariable quantitative ultrasound for diagnosing hepatic steatosis
Jeon, S. K., Lee, J. M., Cho, S. J., Byun, Y.-H., Jee, J. H., & Kang, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-42463-w · Cited by 13
“Mild, moderate, and severe hepatic steatosis were defined using MRI thresholds of 5%, 15%, and 25%, respectively 22 .”
Background
Evaluation and Prognostication of Gd‐EOB‐DTPA MRI and CT in Patients With Macrotrabecular‐Massive Hepatocellular Carcinoma
Cheng, J., Li, X., Wang, L., Chen, F., Li, Y., Zuo, G., Pei, M., Zhang, H., Yu, L., Liu, C., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29052 · Cited by 13
“34 A previous study has shown that intratumor fat is more common in well-differentiated HCC due to transient hypoxia in the early HCC formation.”
Background
Utilizing fully-automated 3D organ segmentation for hepatic steatosis assessment with CT attenuation-based parameters
Yoo, J., Joo, I., Jeon, S. K., Park, J., & Yoon, S. H. (2024) · European Radiology · doi:10.1007/s00330-024-10660-4 · Cited by 13
“These MRI scans encompassed the measurement of MRS-derived PDFF, a non-invasive gold standard for quantifying liver fat [2].”
BackgroundMethod
Emerging mechanisms of non-alcoholic steatohepatitis and novel drug therapies
Hao, C. H. E. N., Zhou, Y., Hao, H., & XIONG, J. (2024) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(24)60690-4 · Cited by 15
Fatty Liver Disease Along Cushing Syndrome Evolution
Marengo, M., Briet, C., Munier, M., Boursier, J., Rodien, P., & Suteau, V. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae568 · Cited by 13
Hepatic steatosis using ultrasound-derived fat fraction: First technical and clinical evaluation
Huang, Y., Cheng, J., Wang, Y., Xu, X., Wang, S., Li, W., & Dong, Y. (2023) · Clinical Hemorheology and Microcirculation · doi:10.3233/ch-238102 · Cited by 12
Fat-only Dixon: how to use it in body MRI
Salari, R., Ballard, D. H., Hoegger, M. J., Young, D., & Shetty, A. S. (2022) · Abdominal Radiology · doi:10.1007/s00261-022-03546-w · Cited by 12
Hepatic Steatosis: CT-Based Prevalence in Adults in China and the United States and Associations With Age, Sex, and Body Mass Index
Guo, Z., Blake, G. M., Graffy, P. M., Sandfort, V., Summers, R. M., Li, K., Xu, S., Chen, Y., Zhou, J., Shao, J., et al. (2021) · American Journal of Roentgenology · doi:10.2214/ajr.21.26728 · Cited by 12
Body composition analysis by radiological imaging – methods, applications, and prospects
Linder, N., Denecke, T., & Busse, H. (2024) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2263-1501 · Cited by 11
“In the future, quantitative CT could also play a greater role in the characterization of MASLD [52].”
Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation
Jeon, S. K., Joo, I., Park, J., & Yoo, J. (2024) · La radiologia medica · doi:10.1007/s11547-024-01833-8 · Cited by 11
“Hepatic steatosis, characterized by excessive fat accumulation in hepatocytes, is a key histological feature of steatotic liver diseases, including metabolic dysfunction-associated steatotic liver disease and alcohol-associated liver disease [1, 2].”
BackgroundMethod
Ultrasound-estimated hepatorenal index: diagnostic performance and interobserver agreement for pediatric liver fat quantification
Hajibonabi, F., Riedesel, E. L., Taylor, S. D., Linam, L. E., Alazraki, A. L., Zhang, C., & Khanna, G. (2024) · Pediatric Radiology · doi:10.1007/s00247-024-06021-4 · Cited by 11
“Magnetic resonance imaging (MRI)–based liver fat quantification techniques like magnetic resonance (MR) spectroscopy and proton density fat fraction (PDFF) have essentially replaced the need for hepatic biopsy for liver fat estimation [8, 9].”
Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity
Zhou, X. J., Lin, X., Chen, J., Pu, J., Wu, W., Wu, Z., Lin, H., Huang, K., Zhang, L. R., Dai, Y.-L., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.986841 · Cited by 11
“Hydrogen proton magnetic resonance spectroscopy ((1)H-MRS) can quantitatively measure liver fat content in a highly accurate and precise manner (17).”
Background
Fat quantification: Imaging methods and clinical applications in cancer
Yan, S. Y., Yang, Y., Jiang, X. Y., Hu, S., Su, Y. Y., Yao, H., & Hu, C. H. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110851 · Cited by 10
Perirenal Fat Thickness is Associated with Metabolic Dysfunction-Associated Fatty Liver Disease in Type 2 Diabetes Mellitus
Yang, J., Li, C. W., Zhang, J. R., Qiu, H., Guo, X. L., & Wang, W. (2023) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s415477 · Cited by 11
“The diagnosis of fatty liver was based on the CTL-S, which was reported to be the most accurate CT index for assessing fatty liver.(16) Participants with 0.”
Method
Two-point Dixon and six-point Dixon magnetic resonance techniques in the detection, quantification and grading of hepatic steatosis
Elfaal, M., Supersad, A., Ferguson, C., Locas, S., Manolea, F., Wilson, M. P., Sam, M., Tu, W., & Low, G. (2023) · World Journal of Radiology · doi:10.4329/wjr.v15.i10.293 · Cited by 10
Malnutrition-Related Liver Steatosis, CONUT Score and Poor Clinical Outcomes in an Internal Medicine Department
Miano, N., Todaro, G., Marco, M. D., Scilletta, S., Bosco, G., Barbagallo, F. D. G., Scicali, R., Piro, S., Purrello, F., & Pino, A. D. (2024) · Nutrients · doi:10.3390/nu16121925 · Cited by 10
“…from steatosis (fatty liver) to steatohepatitis, acute alcohol-associated hepatitis, and liver cirrhosis, is associated with malnutrition, especially among hospitalized patients due to an inadequate intake of both macro-and micro-nutrients, leading to higher mortality and complications [8,9].”
Background
Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis
Park, J., Joo, I., Jeon, S. K., Kim, J., Park, S. J., & Yoon, S. H. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04581-5 · Cited by 10
“Indeed, CT attenuation values, one of the most widely used radiomics features, have been shown to provide a more accurate diagnosis of hepatic steatosis in non-enhanced CT compared to contrast-enhanced CT [10, 11].”
CT assessment of liver fat fraction and abdominal fat composition can predict postoperative liver metastasis of colorectal cancer
Yin, K., Liao, G., Peng, H., Lai, S., & Guo, J. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111814 · Cited by 10
Quantification of Fat Metaplasia in the Sacroiliac Joints of Patients With Axial Spondyloarthritis by Chemical Shift-Encoded MRI: A Diagnostic Trial
Liu, D., Lin, C., Liu, B., Qi, J., Wen, H., Tu, L., Wei, Q., Kong, Q., Xie, Y., & Gu, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2021.811672 · Cited by 10
Liver cirrhosis in children – the role of imaging in the diagnostic pathway
Herrmann, J., Petit, P., Grabhorn, E. F., Lenz, A., Jürgens, J. H. W., & Franchi‐Abella, S. (2022) · Pediatric Radiology · doi:10.1007/s00247-022-05480-x · Cited by 9
“Chemical shift-encoded MRI is the most accurate method to measure fat content [42].”
Method
Performance of the Pooled Cohort Equations in non‐alcoholic fatty liver disease: The Multi‐Ethnic Study of Atherosclerosis
Henson, J. B., Budoff, M. J., & Muir, A. J. (2022) · Liver International · doi:10.1111/liv.15480 · Cited by 9
Mechanisms underpinning the effect of exercise on the non-alcoholic fatty liver disease: review
Bekheit, M., Kamera, B. S., Colacino, L., Dropmann, A., Delibegović, M., Almadhoob, F., Hanafy, N. A. N., Bermano, G., & Hammad, S. (2024) · EXCLI journal · doi:10.17179/excli2024-7718 · Cited by 12
Multi‐Center, Multi‐Vendor Validation of Simultaneous MRI ‐Based Proton Density Fat Fraction and R2 * Mapping Using a Combined Proton Density Fat Fraction‐ R2 * Phantom
Starekova, J., Rutkowski, D. R., Bae, W. C., Do, H. P., Madhuranthakam, A. J., Malis, V., Lin, S. Q., Serai, S. D., Yokoo, T., Reeder, S. B., et al. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29775 · Cited by 10
Surveillance of the progression and assessment of treatment endpoints for nonalcoholic steatohepatitis
Shi, Y., & Fan, J. (2022) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2022.0401 · Cited by 9
“0401 steatosis grade and fibrosis stage.(111) As early as in the FLINT trial of obeticholic acid (OCA), MRI-PDFF was used as a surrogate marker of steatosis.”
Method
Non-Invasive Diagnostic of NAFLD in Type 2 Diabetes Mellitus and Risk Stratification: Strengths and Limitations
Boeriu, A. M., Dobru, D. E., & Fofiu, C. (2023) · Life · doi:10.3390/life13122262 · Cited by 9
“The methods for liver steatosis assessment are based on the chemical shift between water and fat resonance frequencies: magnetic resonance spectroscopy (MRS) and chemical shift encoded MRI (CSE MRI) [124,127].”
BackgroundMethod
Gelatin-Based Liver Phantoms for Training Purposes: A Cookbook Approach
Elisei, R. C., Graur, F., Szold, A., Melzer, A., Moldovan, S. C., Motrescu, M., Moiș, E. I., Popa, C., Pisla, D. L., Vaida, C. L., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13123440 · Cited by 9
An Optimized Strategy Based on Conventional Ultrasound for Diagnosing Metabolic Dysfunction-Associated Steatotic Liver Disease
Feng, X., Ye, J., Deng, H. X., Li, X., Xu, L., Feng, S., Dong, Z., Liao, B., Dong, Z., & Zhong, B. (2023) · Diagnostics · doi:10.3390/diagnostics13233503 · Cited by 8
“As a result, CT is not recommended as a primary modality for measuring liver steatosis [22].”
Background
Motion‐Insensitive Flip Angle Modulated Liver Proton Density Fat‐Fraction and R2 * Mapping During Free‐Breathing MRI in a Clinical Setting
Heidenreich, J. F., Tang, J., Tamada, D., Müller, L., Grunz, J., Souza, R. D. V., Anagnostopoulos, A. A., Pirasteh, A., Reeder, S. B., & Hernando, D. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70033 · Cited by 8
“It is important to recognize that cut-off values for PDFF are not standardized as much as for LIC [2, 41], and both parameters are inherently continuous variables.”
Influential
Association of total and regional fat-to-muscle ratio with the risk of metabolic dysfunction-associated fatty liver disease and other chronic liver diseases
Xi, Y., Liang, Z., Jin, H., Jiang, Y., Tan, R., Zhang, Y., Wang, J., Sui, W., Liu, B., Zhang, H., et al. (2026) · Hepatology International · doi:10.1007/s12072-026-11064-w · Cited by 8
“Liver PDFF and cT1 were obtained from the UK Biobank imaging sub-study [14].”
Diagnostic performance of quantitative ultrasonography for hepatic steatosis in a health screening program: a prospective single-center study
Pyo, J. H., Cho, S. J., Choi, S. C., Jee, J. H., Yun, J., Hwang, J. A., Park, G., Kim, K., Kang, W., Kang, M., et al. (2024) · ULTRASONOGRAPHY · doi:10.14366/usg.24040 · Cited by 8
Recent advances in noninvasive assessment of liver steatosis
Ferraioli, G., & Barr, R. G. (2024) · Polskie Archiwum Medycyny Wewnętrznej · doi:10.20452/pamw.16703 · Cited by 8
Detection of moderate hepatic steatosis on contrast-enhanced dual-source dual-energy CT: Role and accuracy of virtual non-contrast CT
Catania, R., Jia, L., Haghshomar, M., Miller, F. H., & Borhani, A. A. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111328 · Cited by 7
Noninvasive tools for the assessment of fibrosis in metabolic dysfunction-associated steatotic liver disease
Rigor, J., MARTINS, M. E., Passos, B., Oliveira, R., & Martins‐Mendes, D. (2024) · Minerva Medica · doi:10.23736/s0026-4806.24.09290-5 · Cited by 7
Liver and pancreatic fat fractions as predictors of disease severity in acute pancreatitis: an MRI IDEAL-IQ study
Panç, K., Gündoğdu, H., Sekmen, S., Basaran, M., & Gürün, E. (2025) · Abdominal Radiology · doi:10.1007/s00261-025-04809-y · Cited by 7
Using Incidental Radiologic Findings of Hepatic Steatosis to Improve the Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease
Schonfeld, E. A., Kierans, A. S., Fox, R. K., & Brandman, D. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2024.12.014 · Cited by 7
Unraveling the triglyceride-glucose index: a key predictor of liver fat content and the amplifying role of BMI: evidence from a large physical examination data
Liu, S., Duan, J.-H., Chen, Y., Gu, S.-L., Yu-hua, H., Xue, M.-M., & Yue, J.-Y. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1555300 · Cited by 7
Coordination clusters as emerging magnetic resonance imaging contrast agents
Wang, Y., & Zheng, Y. (2025) · Magnetic Medicine · doi:10.1016/j.magmed.2025.100022 · Cited by 7
Multiparametric magnetic resonance imaging may improve detection of patients with at-risk MASH: Real world experience in hepatology clinics
Phipps, M. M., Hage, T., Truong, E., Yang, J. D., Nicolosi, L., Dennis, A., Shumbayawonda, E., Pandya, P., & Wattacheril, J. J. (2025) · Digestive and Liver Disease · doi:10.1016/j.dld.2025.05.023 · Cited by 7
Head-to-Head Comparison between MRI and CT in the Evaluation of Volume and Quality of Epicardial Adipose Tissue
Firouznia, M., Molnar, D., Edin, C., Hjelmgren, O., Östgren, C. J., Lundberg, P., Henningsson, M., Bergström, G. M. L., & Carlhäll, C. (2025) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.240531 · Cited by 7
The impact of concomitant metabolic dysfunction-associated fatty liver disease on adverse outcomes in patients with hepatitis B cirrhosis: a propensity score matching study
Wang, X., Wei, S., Wei, Y., Wang, X., Xiao, F., Feng, Y., & Zhu, Q. (2023) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000002583 · Cited by 7

Authors: Starekova, J., Hernando, D., Pickhardt, P. J., & Reeder, S. B.
Affiliations: University of Wisconsin–Madison (US)
ORCID: Starekova, Jitka
Hernando, Diego
Pickhardt, Perry J.
Reeder, Scott B.
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2021-11
Volume: 301
Issue: 2
Pages: 250-262
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.2021204288
PMC: PMC8574059
OpenAlex: W3199340922
Cited by: 320
References: 117
Citations by year: 2026: 86 · 2025: 96 · 2024: 69 · 2023: 46 · 2022: 22 · 2021: 1
FWCI: 5.32
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Radiology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health contsponsor (R01 DK088925, R01 DK100651, R44-EB025729, R01-DK117354)
Topics: Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: hepatic steatosis, liver fat quantification, non-alcoholic fatty liver disease, chemical shift MRI, non-invasive imaging, cardiovascular risk, metabolic syndrome, type 2 diabetes, artificial intelligence, treatment monitoring
MeSH terms: Fatty Liver, Humans, Liver, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Reproducibility of Results

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Quantification of Liver Fat Content with CT and MRI: State of the Art

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